The transition equations of node j in the bottom-up model are formulated as follows: where x j is the original input vector of node j, E denotes the parameter matrix for transforming this input post,x j is the transformed representation of j, [W * , U * ] are the weight connections inside GRU, and h j and h s refer to the hidden state of j and its s-th child.

h S denotes the sum of the hidden state of all the children of j assuming that all children are equally important to j.


Then the hidden state h j of a node j can be computed by combining the hidden state h P(j) of its parent node P(j) and its own input vector x j .

the transition equations of node j can be formulated as a standard GRU: Through the top-down recursion, the learned representations are eventually embedded into the hidden vector of all the leaf nodes.


Since the num-ber of leaf nodes varies, the resulting vectors cannot be directly fed into a fixed-size neural layer for output.

we add a max-pooling layer to take the maximum value of each dimension of the vectors over all the leaf nodes.


7x7 is the number of regions in the image and 1,024 is the dimension of the feature vector.

each feature vector of V r corresponds to a 32x32 pixel region of the rescaled input image.


It turns out this is due to contextual independence between candidates in the MSMarco dataset, i.e., each candidate is a stand-alone paragraph in this dataset, in contrast to contextually dependent candidate sentences from a document in the NewsQA, SQuAD and WikiQA datasets.

our models (XNET+ and LRXNET) with document reading abilities perform poorly.


Moreover, discrete latent variables are inherently incompatible with backpropagation, since the derivative of the sign function is zero for almost all input values.

the exact gradients of L vae wrt the inputs before binarization would be essentially all zero.


For example, in ALCHEMY, POP actions are sufficient to complete the first steps of good executions.

early during learning, the agent learns a strong bias against PUSH actions.


While powerful, REs have a poor generalization ability because all synonyms and variations in a RE must be explicitly specified.

rEs are often ensembled with data-driven methods, such as neural network (NN) based techniques, where a set of carefully-written rEs are used to handle certain cases with high precision, leaving the rest for data-driven methods.


As for slot filling, however, since the number of mentions of frequent slot labels already exceeds the target shot count, the original slot filling few-shot dataset can be directly used to train existing few-shot learning methods.

we do not distinguish full and partial few-shot learning for slot filling.


This is because feat can use the REtags of all words to generate better context representations through the NN, while logit can only utilize the REtag of the target word before the final output layer.

feat actually gathers more information from REs and can make better use of them than logit.


The amount of extra information that a NN can utilize from the combined REs significantly affects the resulting performance.

the attention loss methods work best for intent detection and feat works best for slot filling.


However, hu16 is a general framework to combine FOL rules, which is more indirect in transferring knowledge from rules to NN than our methods.

it is still inferior to attention loss in intent detection and feat in slot filling, which are designed to combine REs.


Hence, hidden states matrix will have dimension N L x d for tree C L whereas for tree C R , it will have dimension N R x d; where d is dimension of each hidden state.

attention matrix will have a dimension N L x N R .


Formally, where: • h pj ∈ R d represents the final vector representation of node j in tree D p represents column-wise sum Assuming the final vector representation of tree D R is h R , the exact same steps are followed for Phase2 with the exception that the entire process is now conditional on tree D R .

the final vector representation of tree D L , h L , is computed.


Suggestively, the one task where NLI clearly outperforms NMT is WC.

nLI training is better at preserving shallower word features that might be more useful in downstream tasks (cf.


However, the issue is that these distantly-supervised sentences that mention this entity pair may not express the desired relation type.

what our RL agent should do is to determine whether the distantly-supervised sentence is a true positive instance for this relation type.


In other words, we cannot merely use the information of the sentence being processed as the state.

we add the information from the early states into the representation of the current state, in which way to model our task as a MDP problem (Fang et al., 2017).


Rewards As previously mentioned, the intuition of our model is that, when the incorrectly-labeled instances are filtered, the better performance of relation classifier will achieve.

we use the change of performance as the result-driven reward for a series of actions decided by the agent.


Compared to accuracy, we adopt the F 1 score as the evaluation criterion, since accuracy might not be an indicative metric in a multi-class classification setting where the data distribution could be imbalanced.

the reward can be formulated as the Relation Classifier Removed part Figure 2: The proposed policy-based reinforcement learning framework.


The desirable situation is that RL agent has the capacity to increase the performance of relation classifier through relocating incorrectly-labeled false positive instances.

we use the validation set {P ori v , N ori v } to measure the performance of the current agent.


Under the restriction of fixed number, if the agent decides to remove the current state, it means the chance of removing other states decrease.

in order to obtain a better reward, the agent should try to remove a instance set that includes more negative instances.


Therefore, regardless of these easydistinguished instances, the different parts of the removed parts in different epochs are the determinant of the change of F 1 scores.

we definite two sets: where Ψ i is the removed part of epoch i. Ω i−1 and Ω i are represented with the different colors in Figure 2.


However, modeling relations as mappings naturally requires more parameters -a general linear map between d-dimension vectors is represented by a matrix of d 2 parameters -which are less likely to be shared, impeding transfers of facts between similar relations.

it is desired to reduce dimensionality of relations; furthermore, the existence of a composition of two relations (assumed to be modeled by matrices M 1 , M 2 ) matching a third (M 3 ) also justifies dimension reduction, because it implies a compositional constraint M 1 • M 2 ≈ M 3 that can be satisfied only by a lower dimension sub-manifold in the parameter space 1 .


is calculated by u h M r (with each nonzero entry corresponds to an answer).

we have u h M r v t > 0 if and only if h, r, t ∈ T .


This motivates us to use u h M r v t as a natural parameter to model plausibility of h, r, t , even in a low dimension space with d |E|.

we define the score function as for the basic model.


Furthermore, an autoencoder should learn from genuine patterns of relation matrices that emerge from fitting the KB, but not the reverse -in which the autoencoder imposes arbitrary patterns to relation matrices according to random initialization.

it is not surprising that a naive optimization of L 1 + L 2 does not work.


Gaussians of variance 1/d to initialize parameters, so the initial Euclidean norms are and BAm r ≈ √ dc.

by calculating ∇L 1 and ∇L 2 using (1) and (3), we have approximately It suggests that, because of the scaling factor 1 √ dc in (3), we have ∆ 1 and ∆ 2 at the same scale, so we can set λ 1 = λ 2 .


Furthermore, the random baseline RANDOMM2 tests a hypothesis that joint training might be just clustering M 3 and M 1 here, to the extent that M 3 and M 1 are so close that even a random M 2 can give the correct answer; but as it turns out, JOINT+COMP largely outperforms RANDOMM2, excluding this possibility.

joint training performs better not simply because it clusters relation matrices; it learns compositions indeed.


The most popular event schemas, such as ACE, define 33 event types while most visual object training sets contain more than 1,000 types.

methods proposed for zero-shot visual-object classification cannot be directly applied to event extraction due to overfitting.


These relations are especially important for identifying aspect terms and opinion terms (Qiu et al., 2011;Wang et al., 2016), which are also domain-invariant within the same language.

they can be used as "pivot" information to bridge the gap between different domains.


Moreover, g nm is also used as the higher-level feature vector for r nm for predicting the relation label.

the objective for the auxiliary task in (3) becomes: where  Figure 3: An autoencoder for relation grouping.


Learning and planning are accomplished every C steps reset θ Q = θQ 43: end for by the same DQN algorithm, operating on real experience in D u for learning and on simulated experience in D s for planning.

here we only describe the way the simulated experience is generated.


In this setting the dialogue agents are optimized by interacting with user simulators, instead of real users.

the world model is learned to mimic user simulators.


For example, we find in our experiments that at the early stages of training, it is fine to perform planning aggressively by using large amounts of simulated experience even though they are of low quality, but in the late stages of training where the dialogue agent has been significantly improved, low-quality simulated experience is likely to hurt the performance.

in our implementation of Algorithm 1, we use a heuristic 5 to reduce the value of K in the late stages of training (e.g., after 150 epochs in Figure 4) to mitigate the negative impact of low-qualify simulated experience.


To initialize the m-dimensional domain embeddings ed new , we use the column-wise average of all utterance vectors in the training datah avg , and project it to the domain embedding space using a matrix U ∈ R m×m .

the parameters of U * are learned using the column-wise average utterance vectorsh avg j and learned domain vectors for all existing domains d j this is a write-to-memory operation that creates a new domain representation after attending to all existing domain representations.


The judgment score of 3 or above was taken as SUCCESS and 2 or below as DEFECT.

the end-to-end SUCCESS rate, 7 the random prediction accuracy on enabled domains is less than 5% and across the Full domain list is 0.066% 8 Visual inspection of the embeddings confirms that meaningful clusters are learned.


Synchronized attentive information across text and audio would then intuitively help recognize the sentiments and emotions.

we compute a forced alignment between text and audio for each word and propose three fusion approaches (horizontal, vertical, and fine-tuning attention fusion) to integrate both the feature representations and attention at the word-level.


We maximize the lower bound in Eqn 3 exactly by setting q t I to the posterior distribution of the data for the current values of the parameters π (standard E step).

we have We do not need to explicitly compute the posterior distribution q t I = p(I|s) at this point, but only use it to compute the relevant expectation statistics in the M-step.


Then, Equation 2 changes to: Since we do not observe α t z (i.e., which distribution generated a particular feature in a particular document), they are additional latent variables in the model.

we need to introduce a Bernoulli variational distribution q αzt .


In turn, this allows the Sequential-CG component to preserve more probability mass to explain the intermediate content words.

the Sequential-CG component only needs to model words that reflect useful statistical sequential patterns, without expending modeling effort on background content (common words) or noise (rare words).


Then let p r i = s ee {i → r} if i ∈ EE, or p r i = s et {i → r} if i ∈ ET ; similarly, let w r j = I{W i = r} be the indicator variables for W j and q r j be the score for W j = r ∈ R C .

without constraints Y and W Y for now, Eq.


This stark performance gap between humans and machines is not surprising as most powerful language models have been designed to effectively learn local fluency patterns.

they generally lack the ability to abstract away from surface patterns in text to model more complex implied dynamics, such as intuiting characters' mental states or predicting their plausible next actions.


While reliable, these annotations are prohibitively expensive to scale up.

we introduce a new annotation framework that pipelines a set of smaller isolated tasks as illustrated in Figure 3.


Cost The tasks corresponding to the theory category assignments are the hardest and most expensive in the pipeline (⇠$4 per story).

we obtain theory category labels only for a third of our annotated stories, which we assign to the development and test sets.


Given a specific word w, the interaction-based representation e c w for each category c could be very different.

for a specific local context, the extracted relevance signal from a particular convolution filter could be also distinct for different categories.


These models are mainly developed for some specific semantic matching tasks, e.g., paraphrase identification.

information like grammatical structure or sequence of words are often taken into consideration, which is not applicable to seed word based zero-shot document filtering.


We find that the optimal window size is related to the type of task, but affected little by training dataset sizes.

we can search the optimal window size by training on a small dataset.


Recurrent Neural Networks RNN is suitable for handling sequence input like natural language.

many RNN variants are used in text classification.


Similarly to the update gate, the reset gate r t is computed as: We can see that the representation of step t depends upon all the previous input vectors.

we can also express the tth step state shown in Equation 5.


(2018a) quantitatively show that the word-embeddings-based text classification tasks can have the similar level of difficulty regardless of the employed models, using the concept of intrinsic dimension (Li et al., 2018).


The framework relies on so called word embedding: ∆ D → R P , where P is the dimensionality of the embedding space.

the text sequence X is represented via the respective word embedding for each token: A typical text classification method proceeds in three steps, endto-end, by considering a function decomposition , the text sequence is represented as its word-embedding form V, which is a matrix of P × L. • f 1 : V → z, a compositional function f 1 aggregates word embeddings into a fixed-length vector representation z.


Likewise, for the word matters, its general embedding is surrounded by affairs and issues, whereas, its science embedding is closer to particles and material, prompting that it represents substances.

we conclude that domain specific embeddings (here, science), is capable of incorporating domain knowledge into word representations.


In some sense, this 'structure' is represented as a set of hidden topics.

we assume that a document is generated from certain hidden "topics", analogous to the modeling assumption in LDA.


Now we normalize i k asī k so that the importance does not scale with the norm of the word matrix W , and so that the importances of the K topics sum to 1.

the number of topics K is a hyperparameter in our model.


Naturally, a summary relevant to more important topics is more likely to better match the document.

we define r(W, S) as the relevance between the document W and the summary S, and r(W, S) is the sum of topic-summary relevance weighted by the importance of the topic: whereī k is the importance of topic h ⇤ k as defined in (7).


Engineering undergraduate students annotated each pair with the decision whether it was a good match or not and received research credit.

each conceptproject pair received at least three annotations, and upon consolidation, we considered a conceptproject pair to be a good match when a majority of the annotators agreed.


When K is too small, hidden topics are too few to capture key information in projects.

we can see that the increase of topic number from 3 to 6 brings a big improvement to the performance.


For ex-ample, in is recognized as m in the first output and a is recognized as u in the third output, while the second output is correctly recognized.

duplicated texts with diverse errors could serve as complementary information sources for each other.


In average attention combination, each input sequence is treated as equally weighted.

β t,l = 1 N for each input sequence x l .


But it achieves comparable performance with the supervised model when decoding lines with multiple witnesses, since the model could further benefit from jointly aligning and voting among multiple inputs.

with the multi-input attention mechanism introduced in §3.3, we first generate a high-quality consensus correction for each OCR'd line with witnesses via the correction model trained on synthetic data.


These characters are recognized as f in all the witnesses because of similar shape.

the model trained on noisy data are unable to correct them into s. Nonetheless, by removing the factor of long s, i.e., replacing the long s in the ground truth with f, Seq2Seq-Noisy could achieve a CER of 0.062 for single-input decoding and 0.058 for multi-input decoding on the TCP books.


GitHub is the largest open source software forge where anyone can contribute (Ray et al., 2014).

gitHub also contains trivial projects like student projects, etc.


Since we focus on generating the code for every method, we train/test the code prediction task at method level-each method is similar to a sentence and each token in the method is equivalent to a word.

we ignore the code outside the method scope like global variables, class declarations, etc.


Hyperlink contexts usually provide a summary for the target document.

the target document's vector should be impacted by words that others use to summarize it, e.g., paper Papineni et al.


Analogously, pv-dbow uses IN document vector to predict its words' OUT vectors, following the same structure of skip-gram.

in pv-dbow, words' IN vectors are omitted.


For citation recommendation that relies on the compatibility between context words and cited papers, we hypothesize that the IN-for-OUT (or I4O for short) approach will achieve better results.

for w2v-based approaches, we average IN vectors of context words, then score and and rank OUT document vectors by dot product.


The same calculation is performed for b s j and b c j .

we collect knowledge-enriched local inference information: where a heuristic matching trick with difference and element-wise product is used (Mou et al., 2016;.


Take Equation 7as example, r ij is the relation feature between the i-th word in the premise and the j-th word in the hypothesis, but we care more about semantic relation between aligned word pairs between the premise and the hypothesis.

we use a soft-aligned version through the soft-alignment weight α ij .


They assumed that words which have similar semantic meanings are likely to share similar sememes.

they propose to represent word meanings as embeddings (Pennington et al., 2014;Mikolov et al., 2013) learned from a large-scale text corpus, and they adopt collaborative filtering (Sarwar et al., 2001) and matrix factorization (Koren et al., 2009) for sememe prediction, which are concluded as Sememe Prediction with Word Embeddings (SPWE) and Sememe Prediction with Sememe Embeddings (SPSE) respectively.


For example, both characters of the single-morpheme word "徘徊" (hover) represent the meaning of "hover" or "linger".

it is intuitive to take the internal character information into consideration for sememe prediction.


The word "奥斯卡" (Oscar) is created by the pronunciation of Oscar.

the meaning of each character in "奥斯卡" (Oscar) is unrelated to the meaning of the word.


For instance, the word kill may be used much more positively in the context of video games than it would be in a news story; the word soft may be used much more negatively in the context of sports than it is in the context of toy animals (Hamilton et al., 2016a).

lexicon-based analysis exhibits a clear limitation when two groups with strongly dissimilar lexical contexts are compared.


Finally, we eliminate concepts that do not appear in the pre-trained Google News 100B word embeddings.


For example, two popular subreddits, /r/The Donald and /r/SandersForPresident 2 , show only 59.8% and 42.1% in analogy test, respectively.

3 as we proposed, we first create a pretrained word embedding with a larger background corpus and perform additional training with target subreddits.


As only term pairs and the co-occurrence frequencies of them (under specific patterns) are available, we cannot recover the dependency paths between these terms.

we design two features that are similar to those used in (Panchenko et al., 2016;Gupta et al., 2017).


Specifically, we define that an action a t at time step t is to (1) select a term x 1 from the remaining vocabulary V t ; (2) remove x 1 from V t , and (3) attach x 1 as a hyponym of one term x 2 that is already on the current taxonomy T t .

the size of action space at time step t is |V t | × |T t |, where |V t | is the size of the remaining vocabulary V t , and |T t | is the number of terms on the current taxonomy.


tive reward from current time step to the end of an episode would cancel the intermediate rewards and thus reflect whether current action improves the overall performance or not.

the agent would not focus on the selection of current term pair but have a long-term view that takes following actions into account.


Since the parameter updates are performed at the end of each episode, we cache the term pair representations and reuse them when the same term pairs are encountered again in the same episode.

the proposed approach is very time efficient -each training epoch takes less than 20 minutes on a single-core CPU using DyNet (Neubig et al., 2017).


Specifically, we restrict the action space to be the same as the baselines by considering only term pairs in the hypernym graph, rather than all |V | × |T | possible term pairs.

it is possible that at some point no more hypernym candidates can be found but the remaining vocabulary is still not empty.


(2013) find that vector spaces in different languages have similar arrangements.

they propose a linear projection which consists of learning a rotation and scaling matrix.


Negation: Negation is a well-studied phenomenon in sentiment analysis (Pang et al., 2002;Wiegand et al., 2010;Zhu et al., 2014;Reitan et al., 2015).

we are interested in how these four models perform on phrases that include the negation of a key element, for example "In general, this hotel isn't bad".


In the 4-class setup, BLSE only achieves 27.2 F 1 compared to 46.6 or 31.3 of MT and BARISTA, respectively.

presumably, our model does currently not capture the semantics of the target adverbs well.


Our hypothesis is that some relatedness and similarity information is lost during projection.

we visualize six categories of words in t-SNE (Van der Maaten and Hinton, 2008): positive sentiment words, negative sentiment words, functional words, verbs, animals, and transport.


(2014b) develop a neural network model to convey sentiment information in the word embeddings.

the learned embeddings are sentiment-aware and able to distinguish words with similar syntactic context but opposite sentiment polarity, such as the words "good" and "bad".


The method trains the embeddings of the source domain first and then fixes the learned embedding to train the embedding of the target domain.

the learned embedding of the target domain benefits from the source domain.


The word "mysterious" conveys a positive sentiment in the books domain, indicating how wonderful a story is, but it conveys a negative sentiment in the electronics domain typically describing that a product breaks down unpredictably.


We believe that it can achieve greater improvement if it can effectively make use of this information.

in this work, we try to explore a path to use the target domain specific information with as few as possible target labeled data.


The possible explanation is that domains within the same group are more close.

adapting over within group tasks is easier than adapting over cross group tasks, if without any domain adaptation regularizer.


The sentence in Table 1 shows the reviewer's different attitude towards two aspects: food and delivery.

to access how the models perform on review sentences more accurately, we create small but difficult datasets, which are made up of the sentences having opposite or different sentiments on different aspects/targets.


The attention weight score is a global score over the words and the vector dimensions; whereas in our model, there are N word × N filter × N dimension gate outputs.

we train a small model with only one filter which is only three word wide.


However, verbs like walk may have concrete visual representation.

one might perform better overall at translation on concrete words, regardless of part-of-speech.


However, in the real-world, different physicians usually have different writing habits and different x-ray images will represent different abnormalities.

collecting semi-structured reports is less practical and thus it is important to build models to learn from natural reports.


Since tanh(y i + w i ) ∈ [−1, 1], the transformation will automatically satisfy the box constraint.

the constrained optimization problem in (1) is equivalent to In the following sections, we present our designed loss functions for different attack settings.


Applying the loss (7) to (1), the final formulation of targeted caption method given a targeted caption S is We note that (Carlini and Wagner, 2017) has reported that in CNN-based image classification, using logits in the attack loss function can produce better adversarial examples than using probabilities, especially when the target network deploys some gradient masking schemes such as defensive distillation (Papernot et al., 2016b).

we provide both logit-based and probability-based attack loss functions for neural image captioning.


For instance, the captioning model cannot generate a passive-voice sentence if the model was never trained on such sentences.

we need to ensure that the targeted caption lies in the space where the captioning system can possibly generate.


how far is "far" and whether "to the left" is relative to the speaker or the listener) (Shariff, 1998;Landau and Jackendoff, 1993), but our synthetic data lacks such complexities.

our method and results do not automatically generalize to real-life tasks involving natural language.


For ShapeIntersection, the description is given as a sequence of vectors.

two FC layers (with ReLU in between) are used to obtain the initial sentence embeddings.


As reported by (Sukhbaatar et al., 2015;Kumar et al., 2016;Henaff et al., 2016), we also observe that the results of memory networks are unstable across multiple runs.

for each hyperparameter choice, we run all the models 10 times and select the run with the best performance on the validation set.


(2017) reported that the recognition of two-speaker mixtures using a model trained for three-speaker mixtures showed almost identical performance with that of a model trained on two-speaker mixtures.

it may be possible in practice to determine an upper bound on the number of speakers.


However, there are differences in the size of training data and the options in decoding step.


2is unabashedly intractable-given a sequence f , it involves consideration of an exponential (in |f |) number of tag sequences and an infinite number of lemmata sequences.

we will fall back on an approximation scheme (see §5).


We emphasize again that we will train our model in a semi-supervised fashion.

we will assume a set of labeled sentences, D labeled , represented as a set of triples f , , m , and a set of unlabeled sentences, D unlabeled , represented as a set of surface form sequences f .


In the presence of these data, we optimize the supervised sleep phase objective, which is a Monte Carlo approximation of D KL (D labeled || q φ ).

when fitting our variational approximation q φ , we will optimize a joint objective S = S sup + γ sleep • S unsup , where S sup , to repeat, uses actual annotated lemmata and morphological tags; we balance the two parts of the objective with a scaling parameter γ sleep .


The end product of our procedure is a morphological inflector, whose performance is to be improved through the incorporation of unlabeled data.

we evaluate using the standard metric accuracy.


To accomplish this, the model concatenates up to four character output vectors: the {forward, backward} output of the {first, last} character in the token Figure 1a, the four shaded boxes indicate these four outputs for the example token.

the proposed model concatenates all four of these and passes it as input to an multilayer perceptron (MLP): A tag can then be predicted with a linear classifier that takes as input the output of the MLP enized/segmented.


I.e., the loss of each model is minimized independently by separate optimizers with their own hyperparameters.

it is in some sense a multitask learning model and we must define a schedule in which individual models are updated.


First, each word has exactly one syntactic head, and second, the structure is acyclic.

the edges form a directed tree rooted at node 0.


However, exact inference has been shown to be intractable for models that support arbitrary nonprojectivity, except under strong independence assumptions (McDonald and Satta, 2007).

exact inference parsers that support unrestricted non-projectivity are limited to edge-factored models (McDonald et al., 2005;Dozat et al., 2017).


An EC is considered to be correct, only when all the three parts are the same as the corresponding gold standard.

only models involved in dependency structures can be evaluated according to the latter metric.


The example also shows composed transitions that originate from unreachable states.

a large amount of time and memory may be spent creating states and composing transitions that will not be reachable nor needed in practice.


The percentage of non-zero transitions found in the transducers used for testing decreases as the transducer gets larger.

when composing FSTs, we want to construct only reachable states, using a traversal scheme similar to breadth-first search to avoid the storage and computation of irrelevant elements.


Previous work (Harish and Narayanan, 2007) has shown that state queues on the GPU cause a large memory overhead.

if state expansion is moved to the GPU, the structures used to keep track of the states must be compressed or occupy the least amount of memory possible on the device in order to allocate all structures required on the device.


In other words, there lacks a bi-tree aligned data in which each sentence has two syntactic trees complying with two guidelines as shown in Figure 1.

we cannot train a supervised conversion model to directly learn the structural correspondences between the two guidelines.


For each a j ∈ A i , where A i is the set of answer candidates for post p i , we label the pair (p i , q j , a j ) with label y = 0, except for when a j = a i .

for each post p i in our triples dataset, we have one positive sample and nine negative samples.


(2010) created the s-RNN language model given by:  where i(z) maps a hot-one encoded vector to its integer representation.

the U h tensor is composed of |V | recurrence matrices, and at each step of sequence processing the matrix corresponding to the current input is used to transform the hidden state.


They find that language models trained on a number of different languages assign higher probabilities to valid long-distance dependencies than to incorrect ones.

lMs seem able to induce syntactic information despite being provided with no linguistic annotation.


Intuitively, we correlate short sentences with less context and assume that LM scoring will not be able to capture long context.

e-WER is defined as follows: Our model is required to predict two values for each utterance: ERR andN .


Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes.

the authorship of training and evaluation corpora can have unforeseen impacts, including differing model performance for different user groups, as well as privacy implications.


Differential privacy methods are unable to protect the user privacy of h under adversarial attacks, as described in Section 1.

we consider how to learn an unbiased representations of the data with respect to specific attributes which we expect to behave as confounds in a generalisation setting.


We determine the proper weight for combination of DS and generic embeddings in the CCA/KCCA space using the simple optimization problem given in Equation 3.

task specific DA embeddings formed by a proper weighted combination of DS and generic word embeddings are expected to do better than the concSVD embeddings and individual generic and/or DS embeddings and this is verified empirically.


The learned representation is expected to encode information about the themes of the different sections to which the sentence can potentially belong.

thematically related sentences are expected to have similar representations.


During the concatenation of the vectors for words forming triples, the (|T | × 3d)-dimensional vector space S is created.

given the triple t ∈ T , we denote the k nearest neighbors extraction procedure of its concatenated embedding from S as NN S k ( t) ⊆ T .


If we let W ∈ R n×n and U ∈ R n×m then the total number of parameters in the model (excluding the bias terms) is 4(nm+n 2 ) which grows quadratically as n grows.

increases in LSTM size can substantially increase the number of parameters.


The loss at time t is then defined to be L t = − j y j t log(o j t ).

the overall loss is: We use bidirectional LSTMs as our base recurrent unit and use pretrained word embeddings of size 100.


This corresponds to the representation of an edge or a one-length walk between nodes i and j: v Our main aim is to support the relation between an entity pair by using chains of intermediate relations between the pair entities.

the goal of this layer is to generate a single representation for a finite number of different lengths walks between two target entities.


3 Task, Dataset, and Evaluation Metrics In this work we deal with an extrinsic text alignment problem.

we are given pairs of suspicious documents and source candidates and try to detect all contiguous passages of borrowed information.


Moreover, to further strengthen the global information, we implement self-attention (Vaswani et al., 2017) to mine the relationship of the annotation at a certain time step with other annotations.

the gated unit is able to find out both common n-gram features and global correlation.


Moreover, it is important that the transferred text uses a vocabulary that is common in a particular application domain.

unsupervised methods that do not use parallel data are needed to perform this task.


Although the method proposed in this paper can be used to transfer text in both directions, we are interested in transferring in the direction of offensive to non-offensive only.

all the results reported in this section correspond to this direction.


Some human inspection of the filtered corpus indicated that the translations start to be wrong for a threshold larger than 1.0.

we build NMT systems using a filtered version of Common Crawl for thresholds in the range of 0.8 to 1.2 (see Figure 1, green curve).


The class probabilities are calculated as in Equation 2, incorporating also the token position.

for each token we now have a probability distribution over 4n + 1 classes.


they may not be the most important terms in the corpus, but they should weigh α times more than they do now.

for each column m w of document-term matrix M , we apply scalar multiplication: By varying α, we can set the importance of named entities in the corpus and impact the outcome of topic modeling.


Compared to the title, the generated abstracts contain more topically relevant concepts, and can provide better guidance.

we design an Editing Network, which, besides the title, also takes the previously generated abstract as input and iteratively refines the generated abstract.


In such cases, proposed models tend to generate definitions based on the most frequent meaning of the corresponding word.

building models that incorporate word sense disambiguation is an important research direction in natural language processing.


Many natural language processing applications treat words as atomic units and represent them as continuous vectors for further use in machine learning models.

learning high-quality vector representations is the important task.


We use word embeddings (Mikolov et al., 2013) to encode the words present in question, option and the most relevant paragraph.

each word is assigned a fixed d-dimensional representation.


Let q, o i denote the word embeddings of words present in the question and the i th option respectively.

q ∈ R d×lq and o i ∈ R d×lo where l q and l o represent the number of words in the question and option respectively.


(2018), we also observe some variance in model performance even if training is carried out with the same random seed, which is largely due to the non-deterministic ordering of floating-point operations in our environment (Tensorflow (Abadi et al., 2015) with a single GPU).

to account for the randomness, we further train our model 5 times for each random seed and select the model with the best validation performance.


Clearly, the possibility of reusing previously asked questions makes forums much more useful.

many tasks have been proposed to build automatic systems for detecting duplicate questions.


Severyn and Moschitti (2016) showed that relational information encoded in terms of overlapping words between two pairs of text can highly improve accuracy.

for both networks above, we mark each word with a binary feature indicating if a word from a question appears in the other pair question.


2) If the training corpus become smaller after each iteration, the knowledge associated with the removed sentences may be lost over the course of the NMT process.

we adopt the ratio of differences (dif ) between training costs of two training iterations to be the criterion, It should be noted that some of dif x,y are negative.


However, they are kept further training with the ones which have not been learned enough (i.e., high criterion i x,y values).

in this approach, these sentences are deleted for the subsequent iterations.


We assign a normalized weight to each sentence according to the criterion that can be given as: Further, weighted sampling without any replacement was used to select a small subset, such as 80% 1 of the entire corpus, as the corpus D i+1 ws to perform the subsequent iteration.

the updated objective function using weighted sampling J ws can be formulated as follows: only 80% of the entire corpus is used to perform the NMt training during each iteration (for the first two iteration, all of the sentences should be sampled).


In attention-based approaches, the alignment distribution is used to select the positions in the source sentence that the decoder attends to during translation.

the alignment model can be considered as an implicit part of the translation model.


But in the bilingual English-Spanish word embedding space, the word embedding of "EEUU" sits close to the word embedding of "US" and the sum of word embeddings of "Estados Unidos" sit close to the sum of word embeddings of "United States".

a coreference model trained using English-Spanish bilingual word embeddings on English data has the potential to make the correct coreference decision between "EEUU" and "Estados Unidos" without ever encountering these mentions in training data.


Malayalam and Tamil being morphologically rich have low entity overlap (surface level) with Hindi and Marathi.

only 2-3% of Malayalam and Tamil sentences are eliminated from our approach, leading to no gains from multilingual learning.


Although these systems have been widely used in a variety of applications, most of them were built on hand-crafted patterns from syntactic parsing, which causes errors in propagation and compounding at each stage (Banko et al., 2007;Gashteovski et al., 2017;Schneider et al., 2017).

it is essential to solve the problems of cascading errors to alleviate extracting incorrect tuples.


All the three generations only consider binary extractions from the text, while binary extractions are not always enough for their semantics representations.

sRLIE (Christensen et al., 2010) was developed to include an attribute context with a tuple when it is available.


That means the events detected by DEEB-RNN2 are basically the ones with explicit event triggers.

as compared to DEEB-RNN, the precision of DEEB-RNN2 is improved; On the other hand, the above strategy may result in less learning of words, which are event triggers but do not appear in the training dataset.


In practice, because human labeling is expensive and exhausting, one cannot have large scale labeled data for model training.

a common practice is to transform the matching problem to a classification problem with human responses as positive examples and randomly sampled ones as negative examples.


The candidate set fed to the network consists of the selected candidates from the previous turn and candidates from the ontology to a limit K, which restricts the maximum size of the chosen set.

the model does not need an ad-hoc belief state update mechanism like in the NBT.


In this way, the model parameters only learn to model the interactions between turn utterances and ontology terms in the semantic space, rather than the mapping from utterances to states.

information is shared between both slots and across domains.


Thanks to the disjoint training, the learning of slot and value belief states are not restricted to a specific domain.

the model shares the knowledge of slots and values across different domains.


First, we observe that when long news articles in RST-DT have several paragraphs, the discourse relations between paragraphs are very loose and their annotations are not so meaningful.

short texts with obvious logics become our preference.


For instance, in zero-shot image classification, image features are mapped to the text space and the label of the nearest neighbor word is assigned.

the success of such systems relies entirely on the ability of the map to make the predicted vectors similar to the target vectors in terms of semantic or neighborhood structure.


At each time step, we maintain the top b parsing states, pruning off the rest.

a candidate parse that made it to the end of decoding had to survive within the top b at every step.


Different from the essay in language capability tests, academic papers are much longer with much more information, and the overall quality is affected by a variety of factors besides the writing.

we propose a model that considers the overall information of one academic paper, including the title, authors, abstract and the main content of the L A T E X source file of the paper.


the histogram intersection kernel, it is not possible to explicitly define the embedding map (Shawe-Taylor and Cristianini, 2004), and the only solution is to adopt option (i) and compute the corresponding kernel matrix directly.

we combine HISK and BOSWE in the dual (kernel) form, by simply summing up the two corresponding kernel matrices.


Either way, the two approaches, HISK and BOSWE, are fused before the learning stage.

of kernel summation, the search space of linear patterns grows, which should help the kernel classifier, in our case ν-SVR, to find a better regression function.


Since legislators tend to sponsor bills that are ideologically aligned with them, a model trained on a single session will mostly be exposed to bills with a specific ideology on each topic.

a single session model may get the ideology information as an implicit prior without needing to explicitly capture it.


A legislator may be more liberal on one issue and more conservative on another.


Like CTM, Σ is the covariance matrix, modeling the correlation between topics.

as a part of MRF, the unary potential on the topic node z the joint distribution of topics over the phrases and the words in document d are defined as the following equation, where The whole generation process is illustrated in Figure 2(a).


It cannot be directly derived from Σ, which has also been learned in the first stage, because Σ consists the impact from word topics.

given W (P) and β (P) , we use the variational inference again to learn Σ (P) as the illustration of Figure 2(b).


However, these benchmarks were mostly derived independently of any NLP problems.

2 the premisehypothesis pairs were composed under the constraint of predefined rules and the language skills of humans.


2 Therefore, the premisehypothesis pairs were composed under the constraint of predefined rules and the language skills of humans.

while top-performing systems push forward the state-of-the-art, they do not necessarily learn to support language inferences that emerge commonly and naturally in real NLP problems.


We combine the output results on each test set of all 10 folds and then calculate precision, recall and F-score on the combined set.

the scores are comparable to the official results based on the whole test set.


Since short vowels are not typically written in Arabic, many affixes only consist of a word start/end token and one character.

these character ngram parameters outperformed the range of 3 to 6 proposed by Bojanowski et al.


We implemented a deterministic version of PP+EXT but it did not outperform the baseline FT as too many unigrams were lost in longer phrases.

identifying phrases probabilistically is crucial to PP+EXT's high performance.


However, this means that using the nearby context can lead the word vectors to capture some aspects other than style.

as the first extension, we propose excluding the nearby context C near wt from all the context C all wt .


For example, "サンタ (Santa Clause)" and "トナカイ (reindeer)" are topically relevant words and these words tend to appear in a single utterance.

stylistic vectors {x w } using all the context words in an utterance also capture the topic relatedness.


Thus, the precision and recall on neutral class will be largely affected by even a small prediction difference (e.g., with 5 more neutral examples correctly identified, recall is increased by more than 10% on both datasets).

the macro-F1 scores on D3 and D4 are affected more.


First, humor is generally loosely defined.

it is impossible to construct rules to identify humor.


There are also other cases that may have sentiment association between negatively nervous and positively relief or from expected sentiment to unexpected sentiment, which can be explained with relief theory (Rutter, 1997) and incongruity theory (Suls, 1972;Ritchie, 1999).

sentiment association should be a useful representation to reveal the nature of humor.


Second, EDUs can be used as comparable text units and enable us to measure sentiment association among them.


WLD can be freely obtained, however it comes with a price: it is often very noisy.

systems trained only on WLD are at a serious disadvantage compared to systems trained on high quality labeled data, which we term henceforth strong labeled data (SLD).


In the k th blending epoch (k ∈ [0..n−1]) we blend α k of the WLD with the SLD, and feed the data in a random order to the network.

the first blending epoch uses full SLD and full WLD, and in every subsequent epoch the amount of WLD decays by a factor of α.


It seems that the numbers of XX-tags in output and tokens in input should keep consistent for better performance since Seq2seq models look to somehow learn such relationship, and used it during the decoding.

using subword information as features is one promising approach for leveraging subword information into constituency parsing.


However, an efficient chart parser for PCFG can combine only two adjacent constituents in each step.

if the original tree used the rule A → B C D, the parser needs to first combine B with C, say into the substring [i, k], and then the result with D (or vice versa).


As the sentence length increases, parsing times of supertagging together with chart constraints grows much more slowly than the other methods.

we can expect the relative speedup to increase for corpora of longer sentences.


As demonstrated by  and Nickel and Kiela (2017), standard general-purpose representation models trained in an unsupervised way purely on distributional information are unfit for this task and unable to surpass the performance of simple frequency baselines (see also Table 1).

in what follows, we describe a novel supervised framework for constructing task-specific word embeddings, optimised for the graded entailment task at hand.


Across all embedding choices, PCE performs as well or better than F&C, despite the fact that PCE does not use the annotated frames that F&C requires (approximately 188 labels per property).

our approach matches or exceeds the performance of previous work using significantly less annotated knowledge.


Here, the LSTM embeddings perform similarly to the Word2vec embeddings, perhaps because the PROPERTY COM-MON SENSE vocabulary consists of less frequent nouns than in VERB PHYSICS.

the Word2vec embeddings are able to catch up due to their larger vocabulary and much larger training corpus.


We also note that, due to semi-automatic projection of annotations to construct the German dataset, more than half of German sentences do not include labeled predicate and arguments.

while German has almost as many sentences as Czech, it has by far the fewest training examples (predicate-argument structures); see Table 1.


We lowercased the mentions and the names in the entries and chose the entries with the most overlaps.

92.15% and 93.09% of drug mentions in train and test data set matched the DrugBank entries.


As the count-based vectors may overfit to the entity descriptions and to avoid discontinuity in the temporal signatures, we apply exponential smoothing and get smoothed temporal vectors 2 In an analysis of temporal expressions extracted with HeidelTime from the Wikipedia corpus (August 2016 dump), we find that there are on average 18.500 expressions per year value (with year values ranging from 0001 AD to 2050 AD) in contrast to only 9.64 expressions per day value (with day values ranging from 0001-01-01 to 2050-12-31).

using year level identifiers to define our temporal vector space results in short and non-sparse temporal vectors.


Datasets for NED evaluation contain articles published within a short period.

all mentions share a temporal context making it difficult to evaluate temporal variability.


The second best system uses the gold <operation> token only.

knowing the operation type to be performed for a given instance provides valuable information.


This is easily achieved by setting the matrices W and U to 0, and the biases b to the (constant) desired gate values.

all the above RNNs can recognize finitestate languages.


The key difference between RACE and previously released machine comprehension datasets (e.g., the CNN/Daily Mail dataset (Hermann et al., 2015) and SQuAD (Rajpurkar et al., 2016)) is that the answers in RACE often cannot be directly extracted from the given passages, as illustrated by the two example questions (Q1 & Q2) in Figure 1.

answering these questions is more challenging and requires more inferences.


Here, the prompt instructs the workers to make sure the new 'wrong ending' sentence makes sense standalone, that it does not differ in the number of words from the original sentence by more than three words, and that the changes cannot be as simple as e.g., putting the word 'not' in front of a description or a verb.

the workers had much less flexibility for changing the underlying linguistic structures which can help tackle the authorship style differences between the 'right' and 'wrong' endings.


For example, the original tweet (S1) has been modified to (S4), where the hashtag #not disappears.

the hashtag information, the position and the word form of the hashtag (i.e., not, irony, or sarcasm), is missing from the SemEval dataset.


#Disgrace #OngoingProblem http://t.co/FQZUUwKSoN For hashtag disambiguation, the information of the hashtag in each tweet is mandatory.

we recover the original tweets by using Twitter search.


The Penn Treebank Tokenizer provided by NLTK (Bird et al., 2009) is used for tokenization.

each of the left and the right word sequences is encoded as a embedding with a length of 50.


In this paper, we attempt to measure the grammatical completeness of the tweet with and without the hashtag.

language model on the level of POS tagging is used.


We add an equal amount of negative instances randomly selected from the collection of the tweets that do not contain indication hashtags.

the prior-and the post-pruning training data, in the sizes of 28,110 and 9,234, respectively, are prepared for experiments.


The calibrated confidence output by the sigmoid layer of our hashtag disambiguation model can be regarded as a measurement of the reliability of an instance (Niculescu-Mizil and Caruana, 2005;Guo et al., 2017).

we can sort all self-labeled data by their calibrated confidence and control the size of training set by adjusting the threshold.


One root cause of these problems is SQuAD's focus on questions for which a correct answer is guaranteed to exist in the context document.

models only need to select the span that seems most related to the question, instead of checking that the answer is actually entailed by the text.


The model performs matching by stacking multiple interaction blocks in which residual information from one time of interaction initiates the interaction process again.

matching information within an utterance-response pair is extracted from the interaction of the pair in an iterative fashion, and the information flows along the chain of the blocks via representations.


Note that s (k) , s (k+1) , ..., s (k+n) may be the same.

the probability to generate the response u (k+1) is computed as: Figure 1 shows the framework of the proposed Incremental Transformer with Deliberation De- (1) coder.


Supposed the data has maximal m turns and n candidates, the time complexity of crossattention (Zhou et al., 2018), O(mn), is much higher than that of the Dual-Encoder based model, O(m + n).

cross-attention is an impractical operation when the candidate set is large.


We also propose a novel reward function at the surface and semantic levels by utilizing the prior-knowledge of logical form structures.

the primal model tends to generate complete and reasonable semantic representation.


1b), we must remember the placeholders for all the verbs at the same time if we want to obtain the correct predicate-argument structure.

the number of placeholders grows with the length of the sentence; this is only possible with a lambda-style compositional mechanism.


We can map t to this graph by applying the following linear tree homomorphism h into H 2 : A derivation of the form * 0 (t 1 , t 2 ) evaluates to the same graph as t 1 ; the graph value of t 2 is ignored.

if we assume that the subtree of t for cdd evaluates to some arbitrary graph G 0 , the complete derivation t evaluates to G 1 .


If we let CSD 0 be the subset of CSD where all K (x) are zero, we can generalize this construction into an LM-CFTG which generates CSD 0 .

cSD 0 can be generated by a projective grammar that is interpreted into H 2 .


Observe that r t y ≤ m t y by definition, and since t 0 generates at most l 0 x-tokens, by projectivity it generates at most (l 0 +1)m t x x-tokens (one sequence of m t x between each occurrence of x and the next, plus possibly one before the first and one after the last).


Such transition systems can therefore not be extended to transitionbased semantic parsers (Damonte et al., 2017) without (a) losing expressive capacity, (b) giving up compositionality, (c) adding mechanisms for non-projectivity (Gómez-Rodríguez et al., 2018), or (d) using a lambda-style semantic algebra.

our result clarifies how to build an effective and accurate semantic parser.


Whereas V e can be quite large for open domains, or even infinite if it includes sets such as the natural numbers, V a is typically much smaller.

we can effectively learn representations for entities given their set of attributes, from our set of example pairs.


Let x and f denote, respectively, an input sentence and the function defined by a pre-trained generalpurpose sentence encoder.

f (x) represents the continuous embeddings extracted by the encoder.


Although the reconstruction objective can help the binary variable to endow with richer semantics, there is no loss that explicitly encourages the binary vectors to preserve the similarity information contained in the original continuous embeddings.

the model may lead to small reconstruction error but yield sub-optimal binary representations (Tissier et al., 2019).


The semantic-preserving regularizer is then defined as: By penalizing L sp , the learned transformation function g is explicitly encouraged to retain the semantic similarity information of the original continuous embeddings.

the entire objective function to be optimized is: where λ sp controls the relative weight between the reconstruction loss (L rec ) and semantic-preserving loss (L sp ).


Instead, we obtain the predicted relatedness by directly computing the cosine similarity between the continuous embeddings.

there are no classifier parameters for both the binary and continuous representations.


As illustrated in Figure 2, setting a fixed threshold demonstrates better empirical performance on all the datasets.

deterministic threshold is employed for all the autoencoder model variants in our experiments.


Notably, these two strategies exhibit competitive performance even with only 512 bits.

in the case where less memory footprint or little training is preferred, Rand-binary and PCA-binary could be more judicious choices.


The one clear advantage of the graphbased parser is that it performs global inference (but exact search algorithms are already being applied to projective (Shi et al., 2017) and nonprojective (Gómez-Rodríguez et al., 2018) transition systems).

an interesting question is if integrating those two architectures can still be beneficial for the parsing accuracy as in Nivre and McDonald (2008).


We adapt multitasking to heterogeneous tagsets by considering each training dataset, which has a different tag-set T r k , as a separate NER task.

a single model is trained, in which the latent text representation {r i } n 1 (see Sec.


The hierarchy is then used during inference to map fine-grained tags onto a target tag-set.

all tagging decisions are made in the model, without the need for a postprocessing step.


Z y , on the other hand, accumulates scores only of fine-grained tag sequences that agree with y.

this loss function aims at increasing the summed probability of all fine-grained sequences agreeing with y.


Similarly, when informed that an embed-ding is noisy or uninformative, the model should rely more on other features.

we aim to make the model aware of the quality of input embeddings and guide the model to dynamically select and compose features using explicit reliability signals.


Specifically, we assume that if a word occurs more frequently, its word embedding will be more fully trained as it has richer contexts and its embedding is updated more often during training.

we design a set of reliability signals based on word frequency in the embedding training corpus and name tagging training corpus.


On top of the model, a CRF (Lafferty et al., 2001) layer is employed to capture the dependencies among predicted tags.

given an input sequence X and the output of the linear layer Y = {y 1 , ..., y L }, we define the score of a se-quence of predictionsẑ = {ẑ 1 , ...,ẑ L } to be where Aẑ i−1 ,ẑ i is the score of transitioning from tagẑ i−1 to tagẑ i , and y i,ẑ i is the component of y i that corresponds to tagẑ i .


Compared to names, the context usually consists of more common words.

contextual features should be more robust when we apply the model to new data.


For example, 81.3% names are unseen when we train a model on mz and test it on bc.

through cross-genre experiments, we can evaluate the generalization capability of the model.


These models are designed to better capture the context information by pre-training, while our model dynamically composes representations in a reliability-aware manner.

our model and these efforts have the potential to mutually enhance each other.


However, the alignment between a pair of distant languages, which are not in the same language branch 1 , such as Danish-Galician is challenging.

unsupervised translation between distant languages is usually of lower quality.


Even if we just consider 20 languages (M = 20), it will still take 2200 GPU days.

an efficient method for path selection is needed.


However, it is not trivial to apply this methodology to such models.

this paper investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly and consistently improve the performance of widely used NMT models, such as LSTMbased and Transformer-based models.


(2016), in their experiments to compare the results with those of their proposed method.

the focus of the neural models differs from this paper.


Let E 2 R D⇥|Vs| and F 2 R D⇥|Vt| be the encoder and decoder embedding matrices, respectively, where D is the dimension of the embedding vectors.


All of these methods generate triples where the head and tail entities and the predicate stay in their surface forms.

different names and phrases for the same entities result in multiple triples, which would pollute the KG if added this way.


Note that the size ofÃ is the same as the original adjacency matrix A (n × n).

no additional computational overhead is involved.


Then we concatenate the output of each sub-layer to form the new representation.


Therefore, the last K c columns of matrix W a are constant.

the rank of matrix W a • H a is K d + 1 or less, where the K c common topics increase the rank by only one.


As a result, the rank of matrix W a • H a is K d + 1 or less, where the K c common topics increase the rank by only one.

we can replace the last K c columns of W a with a single column equal to the sum of the last K c columns of W a and replace the last K c rows of H a with a single row equal to the sum of the last K c rows of H a .


(2018), in the original WN18 and FB15k datasets there are a large amount of test triples appear as reciprocal form of the training samples, due to the reversible relation pairs.

these authors eliminated the inverse relations and constructed corresponding subsets: WN18RR with 11 relations and FB15K-237 with 237 relations, both of which are free from test data leak.


In this scenario the human teacher marks the correct parts of the machine-generated output y.

every token in the output receives a reward δ t , either δ t = 1 if marked as correct, or δ t = 0 otherwise.


The strong preference for the cheapest feedback option might be a result of overestimating the cost of human post-editing and underestimating the cost of self-training.

a model for field studies where data is limited might greatly benefit from learned estimates of feedback cost and quality improvement .


The nonterminal vectors are calculated only after traversing each subtree.

only the child representations (X c in Eqn.


While this layer allows for information to propagate across long distances, it has a computational and memory cost that scales quadratically with the size of the input sequence.

transformers hardly scale to sequences of more than a thousand tokens.


The memory usage is also dominated by the attention layer as the attention span increase.

reducing the average span will also reduce the memory usage.


However, massive news are generated everyday, making it impossible for users to read through all news (Lian et al., 2018).

personalized news recommendation is very important for online news platforms to help users find their interested contents and alleviate information overload (Lavie et al., 2010;Zheng et al., 2018).


We call this kind of user interests as shortterm interest.

both long-term and shortterm user interests are important for personalized news recommendation, and distinguishing longterm user interests from short-term ones may help learn more accurate user representations.


However, these methods usually learn a single representation vector for each user, and cannot distinguish the long-term preferences and short-term interests of users in reading news.

the user representations learned in these methods may be insufficient for news recommendation.


For example, in the news title "Next season of super bowl games", the local contexts of "bowl" such as "super" and "games" are very important for inferring that it belongs to a sports event name.

we apply a CNN network to learn contextual word representations by capturing the local context information.


In order to handle this problem, in the model training stage, we randomly mask the long-term representations of users with a certain probability p. When we mask the longterm representations, all the dimensions are set to zero.

the long-term user representation in our LSTUR approach can be reformulated as: where B is Bernoulli distribution, and M is a random variable that subject to B (1, 1−p).


This may be because GRU contains fewer parameters and has lower risk of overfitting .

we select GRU as the news encoder in STUR.


When p is too small, the model will tend to overfit on the LTUR, since LTUR has many parameters.


This may be be-    cause the useful information in LTUR cannot be effectively incorporated.


However, the methodology of empirical finance requires interpretable re-sults.

a common approach is to define features for statistical models like Ordinary Least Squares (Lee et al., 2014;Rekabsaz et al., 2017).


We also ignore H4N words that we do not have embeddings for because their frequency is below the word2vec frequency threshold.

we obtain an ADD dictionary which is not a superset of the L&M lexicon because it includes only new additional words that are not part of the original dictionary.


However, there is yet no opensource implementation of their code and there is little knowledge about the performance of newer approaches.

we (1) conduct a reproduction study of Beinborn's (2016) system, (2) evaluate newer neural network architectures, and (3) validate the results on a newly acquired dataset.


In this work, we extend this definition to apply to documents instead of sentences and to include all categories' keywords obtained at this stage.

we calculate a similarity score between each pair of input document -category label keywords using cosine distance and Latent Semantic Analysis.


In the standard text classification datasets used in our experiments, category labels contain less than 5 words so representative documents were not relevant in the enrichment process.

none of the configurations discussed in this section include this step.


First, almost all of the calls are webcast live, and they are later archived on company investor relation (IR) websites or third-party databases.

both audio and text modalities are available so that we can align vocal cues with verbal cues in multimodal learning, and examine the interplay of both modalities and their impact on the financial markets.


However, there is no existing conference call audio dataset.

we set up our S&P 500 Earnings Conference Calls dataset by acquiring audio records and text transcripts from the following two sources.


It is too coarse to extract audio features at the conference call transcript level, and it is also too difficult to segment audio recordings at word level.

we analyze each conference call at sentence level.


We choose the embedding GloVe-300 (Pennington et al., 2014) pre-trained on Wikipedia and Gigaword 5 5 .

each sentence is represented as a 300-dimension vector.


However, every sentence in a conference call is spoken at a distinct time and in a particular order.

it is better to treat a conference call as a sequence of sentences.


Especially in financial markets where CEO's word and voice are closely examined by professional analysts and investors, it is plausible that market reacts to both verbal and vocal signals.

we present a deep model to capture context-dependent unimodal features and fuse multimodal features for the regression task.


Unlike video emotion classification, the regression problem in our study is document-level, which means that we do not make prediction on each utterance.

contextual BiLSTM is not suitable for  stage 2 since the features are already extracted on high-level.


We also experiment with pre-trained word embeddings GloVe-50 and GloVe-100 but find GloVe-300 performs the best among those.

we use GloVe-300 as input word embeddings throughout our experiments.


This phenomenon can be explained by Efficientmarket hypothesis (EMH), which is a theory in financial economics that states that the stock prices only react to new information so it is impossible to predict the stock price based on historical information.

as we target for a longer time horizon, the predictive power of using the previous conference calls information becomes less significant and substantial.


Using unimodal text data only, we may miss the inconsistency in verbal and vocal cues.

the multimodal learning model may capture the inter-dependency between multimodal features and better predict market reactions to earnings conference calls.


However, earnings conference call normally lasts for about one hour or longer.

aligning audio clips with the corresponding text is quite challenging.


In the middle parts, alignment result might be influenced by short syllable words, fast switching of speakers or omission of text record.

we utilize the iterative strategy in segmentation.


Otherwise, the probability of s asserting a false claim of m is 1−Hs |m|−1 .

the joint probability of the observation over X d and y m can be modeled as follows: (3) Then, given all sources S and θ = {H s }, we can write the full joint of direct observations as: Indirect Assertion.


If η d > η i , this means we believe that a direct assertion is more accurate than an indirect assertion, and vice versa.

observing that all sources propose their evidence and make their assertions independently, and taking θ = {H s , R s , Q s , W }, we can write the full joint as: Rs+Qs , we model it by minimizing their KL divergence.


Therefore, observing that all sources propose their evidence and make their assertions independently, and taking θ = {H s , R s , Q s , W }, we can write the full joint as: Rs+Qs , we model it by minimizing their KL divergence.

we also minimize: 2.4 Inference The true claim, y m , is a latent variable that is unknown in our problem, so we solve this ap-proximately by using the EM algorithm (Dempster et al., 1977) to first estimate the true claim, then find the maximum a posterior point estimate of the parameters.


Therefore, we also minimize: 2.4 Inference The true claim, y m , is a latent variable that is unknown in our problem, so we solve this ap-proximately by using the EM algorithm (Dempster et al., 1977) to first estimate the true claim, then find the maximum a posterior point estimate of the parameters.

the E-step is ∀m: v∈m P (y m = v|X, θ t ) (10) In the M-step, besides maximizing the posterior of parameters, we should also consider the interactions between H s and R s , Q s .


We include it as a regularization term with a parameter λ that controls the importance of the interactions.

the M-step is as follows: Since there are no closed form solutions for those parameters, we use gradient ascent to solve them parameter-by-parameter.


However, Sim-Com, which directly counts estimated "correct" claims by MJ-EVI, can improve the estimation.

if we can estimate the veracity of claims accurately, estimating the trustworthiness by claims is more accurate than doing that by noisy evidence.


Such transformations, however, make intermediate features "latent," in the sense that they do not have explicit meaning and are not interpretable.

neural networks are usually treated as black-box machinery.


But it is unrealistic to expect that the content would not flow into the style space simply because of its limited capacity.

we need to design content-oriented losses to regularize the content information.


All prior approaches to GEC consider one sentence at a time and ignore useful contextual information from the document in which it appears, unlike a human proofreader.

cross-sentence context they are not convenient enough.


This task can be formulated as a sequence-to-sequence learning problem where the output sequence corresponds to the position of "positive" sentences in the input email body.

we use a pointer network (Vinyals et al., 2015) to first build hierarchical sentence representations during encoding and then extract "positive" sentences during decoding.


This is because the former involves a single source of synthetic lexical semantic change, while the latter involves two sources (the removal of contexts associated with different senses of a recipient word, as well as the added contexts associated with the senses of a donor word).

this procedure yields less noisy examples of synthetic lexical semantic change.


Moreover, TR is assumed to contribute not only by circumventing the alignment, but also by producing more stable context vectors due to the increased amount of data on which they are trained.

7 we first tease-apart these factors using the following comparisons between the different models.


In contrast, for the SGNS models the introduction of Temporal Referencing circumvents the use of alignment in addition to creating more stable context vectors.

the results support our hypothesis that TR has two complementing factors that improve prior models; firstly, it avoids the need for alignment altogether (and the noise that usually comes with it), and secondly, it produces more stable context vectors due to the increased volume of data when using the full corpus.


Under the assumption that words change their meaning in a systematic way, it follows that words' semantic change would increase over the years.

an ecologically valid model of semantic change should show that the words change more as the time interval for comparison increases, for the vocabulary as a whole.


In the literature, adversarial learning has the difficulty in learning latent representations from discrete structures (e.g., sequence of word embeddings).

most of existing studies in NLP apply adversarial learning with autoencoder-based models, which map a discrete word sequence into a continuous code space beforehand (Makhzani et al., 2015).


makes positive publicity for the tourist attraction in order to attract tourists, which contains a distinct positive emotion.

compared with the low r-values in Dominance in the Essays domain, the r-values in the Travel Guides domain are kept in a good level.


In addition, given that the local interactions, which do not contain equally valuable information, are used as input into ABS-LSTM across time steps, it is understandable that the produced states do not contribute equally to recognizing emotion.

we incorporate two levels of attention mechanism into ABS-LSTM, i.e., Regional Interdependence Attention and Global Interaction Attention.


The output of ABS-LSTM at l th time step is: Global Interaction Attention (GIA): Inherently, LSTM has the capability to 'memorize', and uses the memory to sequentially model long-term dependency.

the hidden states output by ABS-LSTM synthesize the information from current time step's input interaction and that from previous input, respectively.


In this sense, at each time step new information is processed and previous information still exists but is 'diluted' in the hidden state (due to the forget gate).

as some local interactions that are more informative, e.g.


We compare with dTrans as it also utilizes knowledge from document corpora for improving AS performance, which achieves state-of-the-art results.

we compare with the following pipeline methods: CMLA-ALSTM, CMLA-dTrans, DECNN-ALSTM, and DECNN-dTrans.


CNN on the other hand can encode long distance relations existing between concepts.

we use CNN to train the model since C, A, OC and OA can appear a long way away from each other.


In our results, audio features do not help significantly.

we believe that it is necessary to improve the feature extraction for these auxiliary modalities in order to improve the performance further.


The key insight is to label each word with a set of target tags (e.g., B, I, O) as well as a set of polarity tags (e.g., +, -, 0), or use a more collapsed set of tags (e.g., B+, I-) to directly indicate the boundary of targeted sentiment, as shown in Figure 2(a).

the entire task is formulated as a sequence tagging problem, and solved using either a pipeline model, a joint model, or a collapsed model under the same network architecture.


We obtain the unnormalized score as well as the probability distribution of the start position as: where w s 2 R h is a trainable weight vector.

similarly, we can get the probability of the end position along with its confidence score by: During training, since each sentence may contain multiple targets, we label the span boundaries for all target entities in the list T. we can obtain a vector y s 2 R (n+2) , where each element y s i indicates whether the i-th token starts a target, and also get another vector y e 2 R (n+2) for labeling the end positions.


Furthermore, each class in Cap-sNet has distinctive parameters to aggregate features and an independent probability to be existed.

capsNet meets our needs in the transfer learning scenario which includes multiple polarities and tasks.


Specifically, it is prone to overly focus on a few frequent words with sentiment polarities and little attention is laid upon low-frequency ones.

the performance of attentional neural ASC models is still far from satisfaction.


The basic idea behind our approach roots in the following fact: the context word with the maximum attention weight has the greatest impact on the sentiment prediction of an input sentence.

such a context word of a correctly predicted training instance should be taken into consideration during the model training.


The basic intuition of our approach stems from the following fact: in attentional ASC models, the importance of each context word on the given aspect mainly depends on its attention weight.

the context word with the maximum attention weight has the most important impact on the sentiment prediction of the input sentence.


α is a very important hyper-parameter that controls the iteration number of mining attention supervision information (see Line 14 of Algorithm 1).

in this group of experiments, we varied α from 1.0 to 7.0 with an increment of 1 each time, so as to investigate its effects on the performance of our models on the validation sets.


When using our approach, the average attention weight of "uncomfortable" is increased to 2.6 times than that of baseline in these two instances.

tNet-Att(+AS) is capable of assigning a greater attention weight (0.0056→0.2940) to this context word, leading to the correct prediction of the first test sentence.


However, such supervised attention acquisition is labor-intense.

we mainly commits to automatic mining supervision information for attention mechanisms of neural ASC models.


Our method is general for attention mechanisms.

we plan to extend our approach to other neural NLP tasks with attention mechanisms, such as neural document classification (Yang et al., 2016) and neural machine translation (Zhang et al., 2018).


ATE is an extraction or sequence labeling task (Jakob and Gurevych, 2010;Wang et al., 2016a), while ASC is a classification task (Jiang et al., 2011;Wagner et al., 2014;Tang et al., 2016a,b;Tay et al., 2018).

they are naturally treated as two separate tasks, and solved one by one in a pipeline manner.


There is also the latent error propagation when an aspect term is used to classify its corresponding polarity.

due to the different natures of the two tasks, most current works focus either on extracting aspect terms (Yin et al., 2016;Luo et al., 2018; or on classifying aspect sentiment (Ma et al., 2017;Wang and Lu, 2018).


In contrast, the joint approach has separate representations for ATE and ASC and separate labels.

an extra sentiment lexicon can improve the representation of ASC individu- I love the operating system and the preloaded software .


(2) but different propagation directions.

the size of h t is 2d, and the size of d I will also become 2d when stacking a new BiReGU layer.


As previously discussed, the polarity of an aspect term is usually inferred from its related opinion words.

we also use a sentiment lexicon to guide ASC.


(6) as the error to do minimization via back-propagation through time (BPTT) (Goller and Kuchler, 1996).

the loss is as follows: Then, the losses from both tasks and the auxiliary tasks are constructed as the joint loss of the entire model: where L a and L p , which have the same formulation as Eq.


To initialize the domain-specific word embeddings, we train the word embeddings by CBOW (Mikolov et al., 2013) using Amazon reviews 3 and Yelp reviews 4 , which are in-domain corpora for laptop and restaurant respectively.

for S L , we use Amazon embedding, and for S R , we use Yelp embedding.


The existence of short alternatives will be more likely when the target objects are more complex (as in row 2 of Table 1), because these objects require longer utterances (with therefore more short alternatives) to individuate.

we further hypothesize that pragmatic inference will reduce data complexity especially in contexts that elicit more complex language.


Furthermore, our LFs take into account the particular behavior of each relation type, information that expert annotators consider when deciding whether two DUs are attached.

the LFs were divided among the 9 relation types as well as the combination of DU endpoints for each type, e.g.


However, annotating discourse requires considerable effort by trained experts and may not always yield a structure appropriate for the end task.

having a model induce the discourse structure of a text is an attractive option.


In RST, coherent texts consist of minimal units, which are linked to each other, recursively, through rhetorical relations (Mann and Thompson, 1988).

the goal of RST is to describe the rhetorical organization of a text by using a hierarchical tree structure that captures the communicative intent of the writer.


The list for WQ revolves around tech, suggesting the model is learning topics instead of writing quality.


Inspired by models of "rational speech acts", we extend a neural generator to become a pragmatic speaker reasoning about uncertain object categories.

of this reasoning, the generator produces fewer nouns and names of distractor categories as compared to a literal speaker.


(2018), most coreference resolution evaluation measures are not accessible over local decisions, but can only be known until all other decisions have been made.

the next key research question is how to integrate and directly optimize coreference evaluation metrics in an end-to-end manner.


", the connective "so" is identified in the list of connective words for "Contingency" relation and the sentence matches our pattern 1.

we convert this sentence to a "Contingency" discourse relation pair and the two arguments are "they had a $5 off the price" and "i bought it".


Whenever a new cluster is formed it receives a new index, and every mention with y i = receives the index of its antecedents.

the process results in clusters of coreferent entities.


The expected antecedent representation a i of each span i is computed by using the current antecedent distribution P (y i ) as an attention mechanism: The current span representation g i is then updated via interpolation with its expected antecedent representation a i : where is a learned gate vector.

the refined representation g i is an elementwise weighted average of the current span representation and its direct antecedents.


One way to achieve this is by simply representing each mention via the sum of the mentions currently in its coreference cluster.

formally, let C(i) be a coreference cluster (as defined by the antecedent distribution P (y i )) such that i ∈ C(i), and replace Equation 1 with: each span will now contain information about all of its current coreference cluster, effectively equalizing the representations of different spans belonging to the same cluster.


In , entity refinement is more restricted, as it is only obtained from the attention vector at each step.

we believe that our model is the first to use entity-level representations that correspond directly to the inferred clusters, and are end-to-end differentiable.


However, because of potential differences in genre, topic and writing style, such negatives might cause the discriminative model to learn cues unrelated to coherence.

we only select sentences from the same document to construct negative pairs.


Finally, the activation maps of the preceding convolutional block are then reweighted (using the 1 × 128 outputs provided by the excitation layer as weights) to generate the output of the SE block, which can then be fed directly into subsequent layers.

sE blocks are just alternative pathways designed to recalibrate channelwise feature responses by explicitly modeling interdependencies between channels.


Although 6-grams, 7-grams and 8-grams attain almost equally good results, the best choice according to the validation results is to use 6grams.

in the subsequent experiments, we employ the presence bits string kernel based on n-grams of length 6 and KRR with λ = 10 −5 .


Hence, knowledgebased approaches rely on semantic networks such as WordNet 1 (Miller et al., 1990), a manuallycurated resource where synonyms are grouped into so-called synsets, or BabelNet 2 (Navigli and Ponzetto, 2010), a large multilingual encyclopedic dictionary that merges together different resources like WordNet, Wikipedia, Wikidata etc.

in one form or another both approaches to WSD need lexical-semantic data.


Unfortunately, carrying out semantic annotations for a target language requires time, resources and expertise in the field.

in the last few years new approaches have been developed to mitigate the burden of knowledge acquisition by providing automatically or semi-automatically tagged corpora.


Based on this, in what follows we refer to the sentences of a category C as those sentences contained in all the pages of C, and we refer to the occurrences of a lemma in a category C as the occurrences of its inflected forms in the sentences of C. Automatically annotating Wikipedia Our approach aims at creating a sense-annotated corpus in a target language by leveraging the semantic information contained within Wikipedia categories.

by relying on the One Sense per Wikipedia Category assumption (see Section 1), we infer the meaning of words occurring in Wikipedia sentences by exploiting the information provided by their categories.


When we add the logarithmic factor Ψ, instead, the first sense is scored 1.80 while the second is scored 0.69.

for each lexeme-category pair (l, C) we compute the WO between B C , i.e., the BOW representation of the category C (see Section 2.1), and each NASARI vector associated with a given sense of l. Thus, given a set of weighted overlap .


COMPUTING INPUT DE-VICES, POINTING DEVICES, etc.

for each sense s i of l we sample a set of K s i sentences from C 1 , .


Producing the same end result-a shared crosslingual vector space-all CLE models are directly comparable, regardless of modelling assumptions and supervision requirements.

they can support exactly the same groups of tasks.


Supervised projection-based CLEs require merely small-sized translation dictionaries (up to a few thousand word pairs) and such bilingual signal is easily obtainable for most language pairs.

2 despite the attractive zero-supervision setup, we see unsupervised CLE models practically justified only if such models can, unintuitively, indeed outperform their supervised competition.


The problem is that changing the corpus of training and testing may result in different conclusions.

it is less robust than collecting SP pairs by asking expert annotators as (McRae et al., 1998), (Keller and Lapata, 2003), and (Padó et al., 2006), or even asking many ordinary people to vote for a commonsense agreement.


(2018) have also reported similar observations.

it is necessary to move RE forward from sentence level to document level.


The second challenge lies in the large number of fine-grained relation types in our dataset.

it is not feasible for annotators to label relations from scratch.


It should be noted that when a model predicts a wrong relation, we do not know which sentences have been used as supporting evidence, thus the predicted relation instance cannot be classified into the aforementioned subsets and computing precision is infeasible.

we only report recall of the RE model for each subset, which is 51.1% for single, 49.4% for mix, and 46.6% for multiple.


Instead, in Sim, all the candidates are sampled from top 10 similar idioms.

sim is more challenging than Ran as the former has more distracting options.


Considering the intuition gained from the motivating example in Figure 1, it is not surprising that humans would prefer topic models which are locally consistent.

our result that SWITCHP is correlated with human judgments of local topic quality best parallels that intuition.


For example, a restaurant name or a train departure time can contain a large number of possible values.

many of the previous works that are based on neural classification models may not be applicable in real scenario.


As mentioned in Section 1, due to the multi-turn mapping problem, the model should infer the states across a sequence of turns.

we use the recent dialogue history of length l as the utterance encoder input, rather than the current utterance only.


As described above, the output structure predicted by the model forms a tree that should correspond neatly to the input MR, barring some instances of ellipses (as with the date time argument in  Table 6).

4 the input MR can be seen as a constraint on the semantic correctness of the prediction; if the predicted structure doesn't match the MR, the prediction is incorrect and can be rejected.


Let initial word embeddings in a span s i be denoted as x 1 , ..., x T and their encoded representation be x * 1 , ..., x * T .

the weighted embeddings of each spanx i is obtained bŷ where a t is the inner-span attention computed by where α t is a standard feed-forward neural net- Finally, the starting (x * start ) and ending (x * end ) embedding of each span is concatenated with the weighted embedding (x i ) and the length feature (φ(i)) to form its final representation e: the span representation of s and p are marked as e s and e p , respectively.


For example, a mention 'the girls' is labeled as plural and female; we use triplets ('the girls', plurality, Plural) and ('the girls', AG, female) to represent them.

we have 40,149 and 40,462 triplets for plurality and AG, respectively.


In general, the i2b2 dataset seems simpler than the CoNLL dataset, which might because that i2b2 only involves clinical narratives and its training data is highly similar to the test data.

all neural models perform dramatically good, especially on the third personal and possessive pronouns.


The proposed model is an attempt of the general solution of incorporating knowledge (in the form of KG) into the deep learning based pronoun coreference model, rather than using knowledge as features or rules in a dedicated manner.

any knowledge resource presented in the format of triplets, the most widely used entry format for KG, can be consumed in our model with a proposed attention module.


A model of y with access only to the hypothesis side can fit the data perfectly by detecting the presence or absence of c in H, ignoring the more general pattern.

we hypothesize that a model that learns p θ (y | P, H) by training on such data would be misled by the bias c and would fail to learn the relationship between the premise and the hypothesis.


While we could in theory directly parameterize this distribution, for efficiency and simplicity we instead write it in terms of the standard p θ (y | P, H) and introduce a new term to approximate the normalization: Throughout we will assume p(P | H) is a fixed constant (justified by the dataset assumption that, lacking y, P and H are independent and drawn at random).

to approximately maximize this objective we need to estimate p(y | H).


We conduct an experiment using a BERT model without the finetuning process and we find the final dev label accuracy is close to the result from a random guess.

the fine-tuning process rather than sentence embeddings plays an important role in this task.


method used in our document retrieval component could not retrieve the "Food Network" document only from parsing the content of the claim.

the claim verification component can not make the right inference with insufficient evidence.


(2) SherLIiC is harder than existing testbeds because distributional evidence is of limited utility in the classification of InfCands.

sherLIiC is a promising and challenging resource for developing NLI systems that go beyond shallow semantics.


Entailment models built upon cosine similarity are symmetric whereas entailment is not.

many asymmmetric measures based on the distributional inclusion hypothesis have been proposed (Kotlerman et al., 2010;Santus et al., 2014;Shwartz et al., 2017a;Roller et al., 2018).


An additional catch-all category was defined to include symptoms whose status cannot be conclusively inferred from the conversation or which are not relevant to the clinical note.

status may have one of the three values: experienced, not experienced, and other.


One challenge for Seq2Seq models is handling very long inputs (Sutskever et al., 2014).

unlike the span-attribute tagging model where each input example may be a full transcript, we use transcript segments consisting of k consecutive turns.


Our categorization of counseling quality relies on general counseling principles taken from the literature on clientcentered counseling (Miller and Rollnick, 2013).

conversations where the counselor centers on the client and expresses empathy are considered as high-quality interactions.


MI is a well established behavioral counseling strategy that has been successful in achieving behavioral health outcomes (Apodaca et al., 2014).

we restricted our search to video titles indicating that the counseling was conducted using MI and (optionally) including information about the quality of the interaction.


Given our inclusion of the entire dictionary of words and the absence of reliable language labels, we wish to understand how the model performs on uncommon words and sentences.

we rank each word in the dictionary by its frequency over the training set, as a proxy for belonging to highor low-resource languages.


To mitigate this issue and provide more context to negation attributes, we add an additional input, which is the softmax output from entity extraction (Figure 4).

the model learns more about the input as well as the label distribution from entity extraction prediction.


In contrast, our data set contains both same stance pairs, as well as cross stance pairs (i.e., one is supporting and the other is contesting the topic).

it is aligned with the above mentioned task, but in addition, with the task of choosing which side of the debate was more convincing (Potash and Rumshisky, 2017).


does not need acoustic information to detect that the most likely emotion is sadness.

recognizing emotions is not a trivial task because an emotional state can be easily shaped by many factors: context, word content, spectral and prosodic information, among others (Barbulescu et al., 2017).


humanity must further exploit solar energy.


However, the CoPAs can actually provide us with an appropriate context needed for phrasing such arguments.

one could phrase more specific backlash arguments for the CoPA Subsidies or Coercion (with different phrasings), and use them when the CoPA is matched to a motion.


In other words, an efficient argument starts not from the most original and unseen premises, but from what the audience takes as consensual, and only then progresses to what is controversial.

the need for principled arguments is not only a question of time and practicality, but also stems from the essential nature of rhetoric: it is the necessity to call on the general views and opinions shared by everyone and to show that they uphold the desired conclusion.


By choosing the ROUGE F score as ∆, the length of a generated summary will be balanced, though there is no relation with whether or not the summary is overlength.

output length controllable models lose the ability of generating summaries with a desired length.


PG showed state-of-the-art performances on CNN/Daily.


This is because the pyrouge does not support Japanese.

we used sumeval 4 with the MeCab on the ROUGE evaluation of the Mainichi dataset.


LC trained with GOLC sometimes generated much shorter summaries against length constraints.

recall scores were lower and hence F-scores were also lower than those of other methods.